Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks

Reconstruction of gene regulatory networks or `reverse-engineering´ is a process of identifying gene interaction networks from experimental microarray gene expression profile through computation techniques. However, there are some issues and challenges remain in gene regulatory network construction. One of them is the inference complexity due to the high dimensionality of gene expression data. The suggestions for addressing this problem is dimensionality reduction will be applied to the data to reduce the large search space. Many studies have been proposed clustering algorithm to handle the large dimensionality of the data, aiming to improve the accuracy of the inferred network while reducing time complexity. Thus, this paper presents a review of clustering algorithm as dimensionality reduction techniques in the reconstruction of Gene Regulatory Networks. In addition, several new trends were noted to improve the efficiency of clustering algorithms, dimensionality reduction techniques will be employed as clustering algorithm often does not work well for high dimensional data.